COVID-19 Vaccination Opinions in Personal Networks with Replication Code

Replication data for a study on assortative mixing of opinions about COVID-19 vaccination in personal networks. The dataset includes raw data and an R Markdown script with seeded pseudo-random number generators for exact reproducibility of statistical models. The study was authored by Marian-Gabriel Hâncean, Jürgen Lerner, Matjaž Perc, José Luis Molina, and Marius Geantă.

Use Cases

• Replicate statistical models of opinion assortativity based on the provided R code and seeded random generators.
• Analyze patterns of COVID-19 vaccination opinions within personal networks using the raw survey data.
• Study social network structure and homophily related to health attitudes mentioned in the description.
• Validate computational social science methods for reproducible research using the explicitly documented analysis pipeline.

Strengths

• Includes raw data and fully reproducible R Markdown analysis code with explanatory comments.
• Statistical models are made exactly reproducible through explicitly seeded pseudo-random number generators.
• Data corresponds to a peer-reviewed study authored by multiple researchers.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and dataset size are unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

paperswithcode, associated with the study 'Assortative mixing of opinions about COVID-19 vaccination in personal networks'.

Collection Method

Likely collected via survey for the referenced social network study.